Upload 8 files

README.md

@@ -0,0 +1,76 @@
+---
+language: 
+- en
+tags:
+- text-classification
+- zero-shot-classification
+metrics:
+- accuracy
+pipeline_tag: zero-shot-classification
+
+---
+# DeBERTa-v3-base-mnli-fever-anli
+## Model description
+This model was trained on the MultiNLI dataset, which consists of 392 702 NLI hypothesis-premise pairs. 
+The base model is [DeBERTa-v3-base from Microsoft](https://huggingface.co/microsoft/deberta-v3-base). The v3 variant of DeBERTa substantially outperforms previous versions of the model by including a different pre-training objective, see annex 11 of the original [DeBERTa paper](https://arxiv.org/pdf/2006.03654.pdf). For a more powerful model, check out [DeBERTa-v3-base-mnli-fever-anli](https://huggingface.co/MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli) which was trained on even more data. 
+## Intended uses & limitations
+#### How to use the model
+```python
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+model_name = "MoritzLaurer/DeBERTa-v3-base-mnli"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+premise = "I first thought that I liked the movie, but upon second thought it was actually disappointing."
+hypothesis = "The movie was good."
+input = tokenizer(premise, hypothesis, truncation=True, return_tensors="pt")
+output = model(input["input_ids"].to(device))  # device = "cuda:0" or "cpu"
+prediction = torch.softmax(output["logits"][0], -1).tolist()
+label_names = ["entailment", "neutral", "contradiction"]
+prediction = {name: round(float(pred) * 100, 1) for pred, name in zip(prediction, label_names)}
+print(prediction)
+```
+### Training data
+This model was trained on the MultiNLI dataset, which consists of 392 702 NLI hypothesis-premise pairs. 
+
+### Training procedure
+DeBERTa-v3-base-mnli was trained using the Hugging Face trainer with the following hyperparameters.
+```
+training_args = TrainingArguments(
+    num_train_epochs=5,              # total number of training epochs
+    learning_rate=2e-05,
+    per_device_train_batch_size=32,   # batch size per device during training
+    per_device_eval_batch_size=32,    # batch size for evaluation
+    warmup_ratio=0.1,                # number of warmup steps for learning rate scheduler
+    weight_decay=0.06,               # strength of weight decay
+    fp16=True                        # mixed precision training
+)
+```
+### Eval results
+The model was evaluated using the matched test set and achieves 0.90 accuracy.
+
+## Limitations and bias
+Please consult the original DeBERTa paper and literature on different NLI datasets for potential biases. 
+### BibTeX entry and citation info
+If you want to cite this model, please cite the original DeBERTa paper, the respective NLI datasets and include a link to this model on the Hugging Face hub. 
+
+### Ideas for cooperation or questions?
+If you have questions or ideas for cooperation, contact me at m{dot}laurer{at}vu{dot}nl or [LinkedIn](https://www.linkedin.com/in/moritz-laurer/)
+
+### Debugging and issues
+Note that DeBERTa-v3 was released recently and older versions of HF Transformers seem to have issues running the model (e.g. resulting in an issue with the tokenizer). Using Transformers==4.13 might solve some issues. 
+
+## Model Recycling
+
+[Evaluation on 36 datasets](https://ibm.github.io/model-recycling/model_gain_chart?avg=0.97&mnli_lp=nan&20_newsgroup=-0.39&ag_news=0.19&amazon_reviews_multi=0.10&anli=1.31&boolq=0.81&cb=8.93&cola=0.01&copa=13.60&dbpedia=-0.23&esnli=-0.51&financial_phrasebank=0.61&imdb=-0.26&isear=-0.35&mnli=-0.34&mrpc=1.24&multirc=1.50&poem_sentiment=-0.19&qnli=0.30&qqp=0.13&rotten_tomatoes=-0.55&rte=3.57&sst2=0.35&sst_5bins=0.39&stsb=1.10&trec_coarse=-0.36&trec_fine=-0.02&tweet_ev_emoji=1.11&tweet_ev_emotion=-0.35&tweet_ev_hate=1.43&tweet_ev_irony=-2.65&tweet_ev_offensive=-1.69&tweet_ev_sentiment=-1.51&wic=0.57&wnli=-2.61&wsc=9.95&yahoo_answers=-0.33&model_name=MoritzLaurer%2FDeBERTa-v3-base-mnli&base_name=microsoft%2Fdeberta-v3-base) using MoritzLaurer/DeBERTa-v3-base-mnli as a base model yields average score of 80.01 in comparison to 79.04 by microsoft/deberta-v3-base.
+
+The model is ranked 1st among all tested models for the microsoft/deberta-v3-base architecture as of 09/01/2023.
+
+Results:
+
+|   20_newsgroup |   ag_news |   amazon_reviews_multi |    anli |   boolq |      cb |    cola |   copa |   dbpedia |   esnli |   financial_phrasebank |   imdb |   isear |    mnli |    mrpc |   multirc |   poem_sentiment |    qnli |     qqp |   rotten_tomatoes |     rte |    sst2 |   sst_5bins |   stsb |   trec_coarse |   trec_fine |   tweet_ev_emoji |   tweet_ev_emotion |   tweet_ev_hate |   tweet_ev_irony |   tweet_ev_offensive |   tweet_ev_sentiment |     wic |    wnli |     wsc |   yahoo_answers |
+|---------------:|----------:|-----------------------:|--------:|--------:|--------:|--------:|-------:|----------:|--------:|-----------------------:|-------:|--------:|--------:|--------:|----------:|-----------------:|--------:|--------:|------------------:|--------:|--------:|------------:|-------:|--------------:|------------:|-----------------:|-------------------:|----------------:|-----------------:|---------------------:|---------------------:|--------:|--------:|--------:|----------------:|
+|        86.0196 |   90.6333 |                  66.96 | 60.0938 |  83.792 | 83.9286 | 86.5772 |     72 |      79.2 |  91.419 |                   85.1 | 94.232 | 71.5124 | 89.4426 | 90.4412 |   63.7583 |          86.5385 | 93.8129 | 91.9144 |           89.8687 | 85.9206 | 95.4128 |     57.3756 | 91.377 |          97.4 |          91 |           47.302 |            83.6031 |         57.6431 |          77.1684 |              83.3721 |              70.2947 | 71.7868 | 67.6056 | 74.0385 |            71.7 |
+
+
+For more information, see: [Model Recycling](https://ibm.github.io/model-recycling/)

added_tokens.json

@@ -0,0 +1 @@
+{"[MASK]": 128000}

config.json

@@ -0,0 +1,45 @@
+{
+  "_name_or_path": "./results/nli-few-shot/mnli-3c/DeBERTa-v3-base-mnli",
+  "architectures": [
+    "DebertaV2ForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "entailment",
+    "1": "neutral",
+    "2": "contradiction"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "contradiction": 2,
+    "entailment": 0,
+    "neutral": 1
+  },
+  "layer_norm_eps": 1e-07,
+  "max_position_embeddings": 512,
+  "max_relative_positions": -1,
+  "model_type": "deberta-v2",
+  "norm_rel_ebd": "layer_norm",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "pooler_dropout": 0,
+  "pooler_hidden_act": "gelu",
+  "pooler_hidden_size": 768,
+  "pos_att_type": [
+    "p2c",
+    "c2p"
+  ],
+  "position_biased_input": false,
+  "position_buckets": 256,
+  "relative_attention": true,
+  "share_att_key": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.11.0",
+  "type_vocab_size": 0,
+  "vocab_size": 128100
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d60ea56c15d3e58e568dbfc0f2d662b5b3afec5bb0bbe2e10751a4f5a9a6efe7
+size 737726552

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fb31d72b63a8c2996d987db725d4cce57f589959ab4668938a3e0c6a5dd16470
+size 737784811

special_tokens_map.json

@@ -0,0 +1 @@
+{"bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

spm.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
+size 2464616

tokenizer_config.json

@@ -0,0 +1 @@
+{"do_lower_case": false, "bos_token": "[CLS]", "eos_token": "[SEP]", "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "split_by_punct": false, "sp_model_kwargs": {}, "vocab_type": "spm", "model_max_length": 512, "special_tokens_map_file": null, "tokenizer_file": null, "name_or_path": "microsoft/deberta-v3-base", "tokenizer_class": "DebertaV2Tokenizer"}